No Silver Bullet Solution for Customer Churn

Recently Dan Steinman (CMO at Gainsight) wrote about the 3 myths about the indicators of churn when it comes to Customer Success. Dan discussed the myths of predicting churn- Myth #1: product usage, Myth #2: the need for finding leading indicators and Myth #3: if you have enough data/analytics, you’ll figure it out.

My opinion is similar to Dan’s in that every company is different and will have different experiences with the retention of each of their customers. There is no real “silver bullet” to solving customer churn, although there are tools like Customer Success Management solutions that help- but mostly it is about the complete customer experience and all of the associated engagement points.

“The more you engage with customers the clearer things become and the easier it is to determine what you should be doing.”

-John Russell, President, Harley Davidson

The quote above is from a major, well-known American manufacturer- Harley Davidson. It encapsulates what I’ve experienced in managing customers and improving customer retention.

As a Customer Success Manager (CSM), there is one main thing that should always be a priority: observe everything and hear/listen to the customer as much as possible. Observing everything includes all of your organizations interactions with a customer, the customer’s product usage, the customer’s sentiment in social channels, the customer’s financial relationships, the customer’s willingness to be a reference, community participation, executive participation and more. Product usage is just one input in the overall customer experience- so ensure that you listen and observe it, but it is not the silver bullet that many see it as.

I’d like to go through four scenarios that are indicators of churn that won’t ever show up in a product usage review.

Lesson 1: A Single Point of Contact is the Weak Link

Here’s a scenario that I’ve learned from- having a great relationship with a day-to-day contact who was using our software fully- using it heavily in fact and quite pleased with the results and ROI. However, after building what I thought was a great relationship (and which the product usage data would support), during a monthly call I was informed that this contact was leaving the company. The company wasn’t able to immediately fill the role, my emails to other contacts or execs that I hadn’t engaged with went unanswered, and when the renewal came up a few months later- the executives didn’t understand the value of the tool and chose not to renew. It taught me the value of having multi-level relationships- with execs and day-to-day operators. Observation: A single point of contact (no matter how great) can be a point of failure.

Lesson 2: Beware Zombie Customers

Have you ever had the case of the “zombie customer”? “Zombie” customers are those customers that are using your tool and their product usage data may remain strong, but at the end of their subscription they inform you that they were in fact unhappy with the product/services and they had already chosen a competitor and were just waiting to switch over; using the last months on your platform to prepare for the transition.

In this scenario of the “zombie customer” product usage data may miss it entirely. I’d argue that this is where the value of observation across all touch points is useful- look at things like company press releases, competitor press releases, Linkedin connections, and comments you might hear on calls that indicated the customer wasn’t planning on the future because they already knew they were moving to a new system/service. Observation: Product usage may be strong but could miss some things entirely.

Lesson 3: Silent Customers are Churn Threats

Another area of observance that CSMs should have their antennas focused on is unresponsiveness. Do you have customers that have “gone dark” and silent? CSMs should be engaging regularly with customers, on a scheduled cadence and via a variety touch points. Customer’s that stop responding to emails, calls, voicemails, reference requests, NPS surveys- these should be seen as major red flags and potential indicators of risk. Observation: A customer that goes silent is a potentially unhappy customer that needs to be addressed.

Lesson 4: Declining Reference Participation Should Sound Alarms

My last scenario happened less frequently, but was another area that the CSM should be observant of- and it came from our Marketing department. Marketing relies on customers to provide references in order to validate our product/services- and these are key drivers of closing new sales. When a customer is willing to provide a reference or case study, they are typically successful customers. However, sometimes customers that are struggling or not seeing expected value may begin to dial back or refuse your reference requests.

A CSM should have an open line of communication with his company’s Marketing team to be aware of these customer interactions. Marketing can also be a great source of bringing in customer sentiment from social channels such as Twitter and LinkedIn. Observation: Declining reference participation can be seen as an indicator that your customers may be less satisfied or at risk.

How important is data/analytics in determining potential churn?

Data and analytics are similar to product usage information in that they are valuable components of an overall customer experience that helps determine retention.

I’m a huge proponent of data science and analytics and letting the data provide valuable insights that may not be readily apparent.

Here at Marketo, I engaged in several data science projects to better understand factors that indicated potential risk with the goal of predicting and improving retention rates. The data science projects provided valuable insights, but we started with a definitely skewed focus on what were the main indicators of churn/risk.

Data science should be seen as an ongoing initiative and not something that you do once! After some time, we thought- why don’t we look at a cohort of customers that were doing well and analyze their behavior in the first months of their customer experience and how did that compare to those that had churned?

By looking at successful customer journeys we were able to gain great insights that helped in understanding a successful trajectory of customer experience. Our data science efforts have been very helpful in understanding more about our customer experience, but again, they are just another input factor on the overall customer experience.

As Dan correctly wrote in his article, it is really the process and reaction to the data science work and ongoing CSM engagement with the customer that ensues that helps to address potential customer risk and churn.

I think I’ll end with another quote:

“Show me a company with high customer loyalty and low employee engagement – I dare you.”